The Exchanges, every show

Q If you can recall, You know, roughly. Which year did you cross? 50 miller and which year you crossed hundred miller?

A Yeah, yeah. So I believe we crossed fifty million. So I started with Cloudflare in early 14. I think we crossed fifty million in I think end of 16, 2016 in three years. And then in, then at that time we were more than doubling the business. So then we must have crossed the hundred like within the next nine months. Uh, so we went public in 2019 and at that time we were doing about three hundred million. So, so if you know, in our case, I think, uh, we went from like a couple of million to three hundred million in, in a, in a matter of, uh, five years. And, and then after, after going public, we, we actually accelerated over growth, because, you know, as a public company, we were, we, we were able to have bigger awareness, and then COVID happened, and then we went from three hundred million in 2019 to a billion in 2023, basically.

AI assessment note: “crossed fifty million in I think end of 16, 2016 in three years”

Q And your choices of companies have been great, right? You were early member in Splunk, Cloudflare, and Arrive. Like, how did you make those choices?

A Yeah. Yeah. I mean, for me, I, I think sometimes luck plays a role, but I think broadly, when I think about the companies to join, there are a couple of things I always look for. One is, uh, I'm a big believer that if, if the market you are in, if the market has a big opportunity, you as a company will get a lot more chances, uh, to make it work, right? If you're in a, in a market with a small dam, uh, you do, do one thing wrong and you're out of business more than like, right? So, so for me, um, when I chose Splunk, I just felt that big data was something up and coming, and I felt that, you know, everything up to that point was structured data indexing, and Splunk was the first company where you can index unstructured data and make it searchable, and I just felt that the time of this is massive, and Splunk was doing great work. Similarly for Cloudflare, the market, the initial market of Cloudflare was in the tens of billions of dollars, and Cloudflare found, actually, I felt found this very niche market where Anybody on the internet who doesn't have the scale like the larger companies, you know, like think about the Yahoo's or the Googles of the world, but they still need their website to be fast. They need the website to be secure. Their website to be online. And there was nobody serving that. And I just felt that the market is big and it's just underrepresented market. And t…

AI assessment note: “when I think about the companies to join, there are a couple of things”

Q But isn't it too early to, to build a global go-to-market?

A Right, right. I think in our case, what happens, the customers are pulling into that direction, right? So for us, for example, uh, like we have a lot of customers out of UK now, and there's just massive demand. Similarly, uh, even before we went to Singapore, we now have a lot of, uh, like big customers in Asia, and, and they pulled us in. So it's more that instead of us intentionally, you know, going and winning the market, it was the customer pulled us into those markets. Basically, you know, I'm a big believer in, in, you know, like, you know, when you think about the go-to-market is that you never want to go into a market cold, you know, because, you know, what happens when you go into the market cold is that you have to invest a lot of amount of money to warm that market, you know, from a marketing perspective, you have to hire the sales team and whatnot, and it takes them a while to get the first customer. So if you think about, you know, you added all this cost into the model, right? Marketing costs and the people costs and whatnot, but the revenue comes like six, nine months or 12 months from there. And if you, if you, for whatever reason you picked the wrong market, now you wasted time and you wasted a lot of money, right? So, so if you can, uh, find like a traction in the market, uh, for whatever reason, in our case, we found traction in like, like Asian market and, a…

AI assessment note: “instead of us intentionally... going and winning the market, it was the customer pulled us”

Q And now let's step into your heart as an angel. So you would have received like in the last couple of years, uh, maybe a few hundreds or thousands of pitches from founders. How did you choose the 15 companies that you choose to invest in?

A Yeah, so for me, I, how I chose, um, I chose the company where I understood, I understood the space. I think, you know, like example-wise, um, uh, like Atomic Works, for example, right? Um, I invested because I, when I, I, I understand ITSM. I understand that, uh, ITSM is a space where there's, there's a bunch of steps needs to be done every time, and agents can easily do it, right? So I understand space, and I understand the application, and I see the pain, and then, Um, the, I, I know the founders really well, so I just felt that this feels right. So, so for me, it's like, if I really understand the space and understand what is agents trying to do and I can wrap my head around, then I'll go invest in it. If I cannot, like, then I, I just won't basically, right? I mean, if, if, if you come and say that, oh, this agent can do, uh, the XYZ in healthcare, I don't understand healthcare, so I don't know, right? I remember somebody pitched me this, uh, idea of this agent, uh, can read the x-rays and, uh, And, uh, you know, in a typical way, when, you know, when the x-ray happens, you, uh, the doctor only look at the area where you have the pain and you just check that and it doesn't look at the rest of the x-ray. So it's just, you know, there's a lot of information is wasted away. And the idea was that this agent can read the whole x-ray and create a database. And actually then, uh,…

AI assessment note: “how I chose, um, I chose the company where I understood, I understood the space.”

Q And why join a company whether now you had, like, large credibility of you could have started your own company?

A Right, right, yeah. I mean, I think it's just, I could have, but I just never, you know, I'm not very technical, even though I have engineering background, right? So one is I need a technical co-founder to do something. I just never click with somebody to the level that we could start something. So it's, it's very much that versus, it's not like any calculative thing. It's just, I never got to it. You know, if it happens in the future, it's great. I love building businesses. I mean, don't get me wrong. I love, I get so much joy out of it. So, so let's see how things go. I mean, I'm happy at Arise. I think it's a great company. I think I found home here. So, I do want to, you know, build this business and, and, you know, hopefully help make another iconic company.

AI assessment note: “I need a technical co-founder to do something. I just never click with somebody”

Q And this, these agents are for DoorDash are created by Arise? Like, or you are just.

A So, so we, so agents are created by, uh, so we are the, the layer for evals and observability. So agents, Do created by, you know, they can, you can create agents through, like, frameworks like, let's say, Langchain or, or CrewAI or whatnot, or you can actually, you know, uh, just, uh, write your own engine, but we are the evals and observability layer for those agents to make sure that, because if you think about, right, if agents, if, if DoorDash agents start to perform, uh, like, start hallucinating, and, and let's say, if they start accepting all their events, or if they start rejecting all their events, It's a big problem for DoorDash, so they actually have to make sure that agent is doing what it's supposed to do, and, and, and it's accurate, you know, so, so that's why observability becomes so important, like, if you can't, you can't just allow yourself to, to be in a black box like that, and we have seen so many examples, right, in Air Canada, which is a customer of Arise, before they were Arise customer, they launch an agent Uh, to help with booking the tickets, and, and, and it was mainly around, uh, booking the tickets through miles, and, and people find a way to, like, give it prompts where actually the agent end up issuing miles to those customers, to those customers, and then those customers use those miles to buy, start buying tickets, and Air Canada had no idea …

AI assessment note: “we are the evals and observability layer for those agents”